Financial Engineering

The stability of financial markets benefits billions of people. In order to respond to the challenge of maintaining healthy and stable markets, today’s systems engineers must possess quantitative and business know-how to understand and manage the complexity of financial instruments and inter-bank dynamics.

Systems engineers master the core skills of modelling economic and human behaviours, and provide insights regarding how to reach economic, social and individual investors’ objectives.

Financial engineering covers modelling, analysis, implementation of financial decision making and risk management. More than just theories, systems engineers develop practical tools with a combination of multiple disciplines including statistics, probability, optimization and stochastic analysis. Related research topics include pricing and hedging, systematic risk management, stochastic volatility models, and portfolio choice.

Analysis of Drawdown Risk

L. Li Investors demand protection against sharp price falls, which are not uncommon in financial markets. A key concept in measuring the severity of price drops is drawdown, which shows how much the asset price falls relative to its historical maximum....

Data-Driven Deep Learning Methods for Financial Decision Making

L. Li A model based approach is typically adopted for solving financial decision making problems, which is prone to model error. In this project, we develop a data-driven approach that is free of parametric models and we use neural networks to approximate...

First-Loss Capital

X. He In most U.S. hedge funds, the managers take a performance fee, such as 20%, for any profit they generate for the investors but do not pay in case of a loss. In China private equities and also in some new hedge funds in the United States, the...

Hedging Periodic Cashflow

C. Yang Financial products such as Leveraged ETFs involve the hedging of an infinite-horizon cashflow stream, where the hedging occurs in continuous time while the hedging performance is monitored periodically at discrete time points. Traditional...

High Frequency Trading

N. Chen High frequency trading (HFT) is to use computers to process market information and make elaborate decisions to "initiate buy/sell orders. As of July 2009, HFT firms account for 73% of all US equity trading volumes." We study how to develop...

Limit order books

X.F. Gao As a trading mechanism, limit order books have gained growing popularity in equity and derivative markets in the past two decades. The objective of this project is to understand deeper on different time scales, how the price is...

Markov Chain Approximation for Option Pricing and Hedging

L. Li Markov chain approximation provides a general approach to handle Markovian asset price models in a unified and efficient way. In this project, we develop algorithms using Markov chain approximation for pricing and hedging exotic options with complex...

Mining Streams of Financial Data and News

J. Yu Financial market trends prediction is a technique to forecast market trend changes, which assists financial market participants to spot arbitrage opportunities for investment. Currently, most existing reported data mining studies for trend...

Multivariate Stress Scenario Selection

D. Ahn In modern financial systems, stress testing has been considered an important tool to figure out the effect of multiple economic factors on the stability of financial institutions. In usual stress testing, by applying extreme-yet-plausible stress...

Realization Utility

X. He Individual investors derive realization utility: every time they buy a stock, an investment account is created in their mind and will be closed when the stock is sold. They feel good with a realized gain and bad with a realized loss. In this...